| 1. |
- Andersson, Roger, et al.
(författare)
-
Large Housing Estates in Sweden. Overview of developments and problems in Jönköping and Stockholm
- 2003
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- All over Europe massive numbers of people live in large-scale housing estates built after the Second World War. The estates were carefully planned, but now often manifest a mulitude af problems. They house large numbers of low-income households, the unemployment rates are above average and in some countries they have become concentration areas for ethnic minorities. Many estates are becoming increasingly associated with crime and social exclusion. The circumstances on the estates and policy initiatives associated with these are focus of the RESTATE-project. RESTATE is the acronym for Restructuring Large-scale Housing Estates in European Cities: Good Practices and New Visions for Sustainable Neighbourhoods and Cities. The study draws on estates in ten European countries: France, Germany, Hungary, Italy, the Netherlands, Poland, Slovenia, Spain, Sweden and the United Kingdom. The present report deals specifically with large housing estates in two cities in Sweden: Jönköping och Stockholm. The basic questions are about physical structure, demographic-, economic- and socio-cultural developments. The same kind of information for estates in other countries in the RESTATE-project can be found in the parallel reports
|
|
| 2. |
- Bergström, Sara, et al.
(författare)
-
Miljöbedömning och miljöbeskrivning i väg- och järnvägsprojekt : Vägledning
- 2022
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Denna vägledning förmedlar Trafikverkets syn på hur de båda processerna miljöbedömning och miljöbeskrivning bör tillämpas i planläggning och projektering av väg- och järnvägsprojekt, med utgångspunkt från lagkrav, praxis och Trafikverkets erfarenheter. Den tar även upp innehållet i de båda dokumenten miljökonsekvensbeskrivning och miljöbeskrivning som kommer ut av de båda processerna. Vägledningen syftar till att bidra till integrering av miljöaspekter i planläggning och projektering av vägar och järnvägar samt till en god kvalitet på genomförande och redovisning. Den syftar även till att miljöbedömningar och miljöbeskrivningar ska genomföras på ett likartat sätt i hela Trafikverket och få en jämn kvalitet. Vägledningen riktar sig främst till Trafikverkets konsulter och egna miljöspecialister som gör miljöbedömningar och miljöbeskrivningar i Trafikverkets väg- och järnvägsprojekt. Vägledningen ersätter Trafikverkets Handbok om metodik för miljökonsekvensbeskrivning för vägar och järnvägar (Trafikverket 2011:090). Den ersätter också texter om MKB och miljöbeskrivning i Trafikverkets Rapport planläggning av vägar och järnvägar. Vägledningen innehåller bland annat:relevant lagstiftning och målbeskrivning om hur miljö integreras samt miljöbedömningens roll och moment i planläggningens olika skedensamråd, samrådsunderlag, utredning om betydande miljöpåverkan och miljökonsekvensbeskrivningens (MKB) innehåll Trafikverkets syn på hantering av miljöaspekter, behov av att koppla dessa till miljöintressen för vilka konsekvenser beskrivs
|
|
| 3. |
- Nilsson, Jonas, 1979, et al.
(författare)
-
Pedestrian Detection using Augmented Training Data
- 2014
-
Ingår i: Proceedings - International Conference on Pattern Recognition. - 1051-4651. - 9781479952083 ; , s. 4548-4553
-
Konferensbidrag (refereegranskat)abstract
- Detecting pedestrians is a challenging and widely explored problem in computer vision. Many approaches rely on large quantities of manually labelled training data to learn apedestrian classifier. To reduce the need for collecting and manually labelling real image training data, this paper investigates the possibility to use augmented images to train a pedestrian classifier. Augmented images are generated by rendering virtual pedestrians onto real image backgrounds. Classifiers learned from real or augmented training data are evaluated on real image test data from the widely used Daimler Mono Pedestrian benchmark data set. Results show that augmented training data generated from a single 200 frame image sequence reach 70% average detection rate at one False Positives Per Image (FPPI), compared to 81% for a classifier trained by a large-scale real data set.Results also show that complementing real training data withaugmented data improves detection performance, compared tousing real training data only.
|
|
| 4. |
- Andersson, Martin, et al.
(författare)
-
Dynamic pH determination at high pressure of aqueous additive mixtures in contact with dense CO2
- 2018
-
Ingår i: Journal of Supercritical Fluids. - : Elsevier BV. - 0896-8446 .- 1872-8162. ; 136, s. 95-101
-
Tidskriftsartikel (refereegranskat)abstract
- A system consisting of a high-pressure tolerant microfluidic glass chip, high-speed absorbance imaging, and image processing has been developed to study rapid dynamic events like pH change in a multiphase flow. The system gives both kinetic and quantitative equilibrated information. By tracking the interactions of aqueous additive mixtures and liquid CO2, at 80 bar and 24 °C, under flow, measurement at a given P, T condition is done in 0.25 s. The acidification rate to steady state was found to be mass transport limited, occurring in less than 1 s. For 30 mM of the additives ammonium acetate and ammonium formate, equilibrium pH of 4.5 and 4.1, respectively, was seen. These additives are of key importance in common mobile phases used in SFC.
|
|
| 5. |
- Bernstad, Anna, et al.
(författare)
-
Potentials for food waste minimization and effects on potential biogas production through anaerobic digestion.
- 2013
-
Ingår i: Waste Management & Research. - 1096-3669. ; 31:8, s. 811-819
-
Tidskriftsartikel (refereegranskat)abstract
- Several treatment alternatives for food waste can result in both energy and nutrient recovery, and thereby potential environmental benefits. However, according to the European Union waste management hierarchy, waste prevention should be the prioritized strategy to decrease the environmental burdens from all solid waste management. The aim of the present study was therefore to investigate the potential for food waste minimization among Swedish households through an investigation of the amount of avoidable food waste currently disposed of. A further aim was to investigate the effect on the national biogas production potential through anaerobic digestion of food waste, considering minimization potentials. A method for waste composition analyses of household food waste, where a differentiation between avoidable and unavoidable food waste is made, was used in a total of 24 waste composition analyses of household waste from Swedish residential areas. The total household food waste generation reached 3.4 kg (household and week)(-1), on average, of which 34% is avoidable. The theoretical methane (CH4) potential in unavoidable food waste reached 442 Ndm(3) (kg VS)(-1) or 128 Nm(3) tonne(-1) wet waste, while the measured (mesophilic CH4 batch tests) CH4 production reached 399 Ndm(3) (kg VS)(-1), which is lower than several previous assessments of CH4 production from household food waste. According to this study the combination of a decrease in food waste generation-in case of successful minimization-and decreased CH4 production from unavoidable food waste will thus result in lower total potential energy recovery from household food waste through anaerobic digestion CH4 potential than previously stated.
|
|
| 6. |
- Bendazzoli, Simone, et al.
(författare)
-
Automatic rat brain segmentation from MRI using statistical shape models and random forest
- 2019
-
Ingår i: MEDICAL IMAGING 2019. - : SPIE-INT SOC OPTICAL ENGINEERING. - 9781510625464 - 9781510625457
-
Konferensbidrag (refereegranskat)abstract
- In MRI neuroimaging, the shimming procedure is used before image acquisition to correct for inhomogeneity of the static magnetic field within the brain. To correctly adjust the field, the brain's location and edges must first be identified from quickly-acquired low resolution data. This process is currently carried out manually by an operator, which can be time-consuming and not always accurate. In this work, we implement a quick and automatic technique for brain segmentation to be potentially used during the shimming. Our method is based on two main steps. First, a random forest classifier is used to get a preliminary segmentation from an input MRI image. Subsequently, a statistical shape model of the brain, which was previously generated from ground-truth segmentations, is fitted to the output of the classifier to obtain a model-based segmentation mask. In this way, a-priori knowledge on the brain's shape is included in the segmentation pipeline. The proposed methodology was tested on low resolution images of rat brains and further validated on rabbit brain images of higher resolution. Our results suggest that the present method is promising for the desired purpose in terms of time efficiency, segmentation accuracy and repeatability. Moreover, the use of shape modeling was shown to be particularly useful when handling low-resolution data, which could lead to erroneous classifications when using only machine learning-based methods.
|
|
| 7. |
- Plattén, Michael, et al.
(författare)
-
Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis.
- 2021
-
Ingår i: Journal of Neuroimaging. - : Wiley. - 1051-2284 .- 1552-6569. ; 31:3, s. 493-500
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND AND PURPOSE: Corpus callosum atrophy is a sensitive biomarker of multiple sclerosis (MS) neurodegeneration but typically requires manual 2D or volumetric 3D-based segmentations. We developed a supervised machine learning algorithm, DeepnCCA, for corpus callosum segmentation and relate callosal morphology to clinical disability using conventional MRI scans collected in clinical routine.METHODS: In a prospective study of 553 MS patients with 704 acquisitions, 200 unique 2D T2 -weighted MRI scans were delineated to develop, train, and validate DeepnCCA. Comparative FreeSurfer segmentations were obtained in 504 3D T1 -weighted scans. Both FreeSurfer and DeepnCCA outputs were correlated with clinical disability. Using principal component analysis of the DeepnCCA output, the morphological changes were explored in relation to clinical disease burden.RESULTS: .76%, for intracranial and corpus callosum area, respectively through 10-fold cross-validation). DeepnCCA had numerically stronger correlations with cognitive and physical disability as compared to FreeSurfer: Expanded disability status scale (EDSS) ±6 months (r = -.22 P = .002; r = -.17, P = .013), future EDSS (r = -.26, P<.001; r = -.17, P = .012), and future symbol digit modalities test (r = .26, P = .001; r = .24, P = .003). The corpus callosum became thinner with increasing cognitive and physical disability. Increasing physical disability, additionally, significantly correlated with a more angled corpus callosum.CONCLUSIONS: DeepnCCA (https://github.com/plattenmichael/DeepnCCA/) is an openly available tool that can provide fast and accurate corpus callosum measurements applicable to large MS cohorts, potentially suitable for monitoring disease progression and therapy response.
|
|
